โจThe Beginner's LLM Development Journey
Journey Overview
As a developer first venturing into the world of Large Language Models (LLMs), you're likely excited to learn how to create projects using this technology. This guide will introduce you to the essential knowledge you need before and during your project development.
We'll provide an overview of the LLM knowledge journey, helping you become a proficient LLM Developer.
Welcome to the first version of our guide, released in August 2024. This is just an overview to get you started. We'll be adding more details and examples to each section soon. Keep checking back for updates that will help you on your LLM development journey!
Stage 1 : Exploration
The first step of your journey involves exploring familiar concepts.
Objective
Learn about
Glossary
How LLM is work ?
How LLM API is work ?
LLM behaviour
Prompt and Completion
LLM APIs
To begin using LLMs, you should first explore the models available. There are numerous options, but starting with popular models like GPT-4o (from OpenAI) is recommended. Alternatively, you can use our "LLM as a service" offering, which you can learn more about through our resources.
LLM as a serviceLearn how to get OpenAI and Anthropic API key from this.
Once you've obtained your API key, test it using the example commands provided by the LLM provider to ensure you receive a response.
LLM Behaviour
๐Variableโ๏ธConditionโLoopProject Example
The fastest way to learn project setup and usage is by studying existing projects or cookbooks from LLM providers. This approach will help you better understand the capabilities of LLMs.
Stage 2 : In-Depth Study
After gaining a basic understanding of how to use LLMs, it's time to go deeper into LLM-specific knowledge
Objective
Learn about
Prompt structure
Prompt technique
Recursive prompt
Framework
Prompt Engineering
Mastering prompt engineering is crucial, as the performance and quality of LLM outputs heavily depend on the prompts you provide. Begin with basic prompting techniques, such as standard usage, role assignment, and task definition. Then, progress to advanced prompting methods like Chain of Thought (CoT) and Few-shot learning.
Prompt technique
๐จDemonstration๐Formatting๐ฃChat๐Technical term (Retrieve)Retrieval Augmented Generation (RAG)
RAG is an essential technique for improving LLM performance. It addresses the limitations of pre-trained LLMs, which may have outdated knowledge or lack domain-specific information. RAG allows you to augment the LLM's knowledge base with custom data.
We offer a RAG example focusing on Thai language implementation, which you can explore to learn more about this technique.
Frameworks
Implementing RAG from scratch can be time-consuming. To expedite development and simplify the process, consider using frameworks like LlamaIndex. When working with these frameworks, focus on text processing and data ingestion pipelines, as these are critical components of effective RAG implementation.
By mastering these areas, you'll significantly enhance your ability to create high-performing LLM applications.
Stage 3 : Development
Objective
Learn about
How to integrate LLM with other system
How to define the performance metrics
How to debugging LLM application
Applications
It's time to start developing your project. Develop a Simple LLM Project, applying the techniques learned in previous stages to enhance accuracy.
Apply RAG techniques to your specific data or database. Evaluate whether it's working effectively or if adjustments are needed.
Conduct a simple evaluation of the results.
Explore advanced RAG methodologies or other techniques to further improve your LLM's accuracy.
Deploy your RAG system in your personal environment. This step helps you understand the practical aspects of running an LLM application.
By following this development stage, you'll gain practical experience in building and refining LLM applications, setting a strong foundation for more complex projects in the future.
The stages beyond this point are tailored for individuals aiming to deploy their LLM projects for public use or within corporate environments.
Stage 4 : Proof of Concept (POC)
Objective
Learn about
Business use case
Monitor and Log system
How to measure the succeed of the POC
Project
At this stage, you're ready to approach your LLM project more seriously, especially if working with a team. Your goal is to create a compelling proof of concept that demonstrates the project's viability and potential value.
In addition to developing your project with consideration for the techniques learned in previous stages, there are additional tasks you need to focus on:
Find and document specific business use case scenarios where your LLM application can provide significant value. This evidence will help in gaining buy-in from stakeholders.
Implement accuracy evaluation protocols to formally assess the accuracy of your LLM application.
Based on evaluation results, refine and improve your system to meet or exceed accuracy expectations.
Explore and implement observability and traceability methods for monitoring and tracking the performance of your RAG system. This includes logging, performance metrics, and tools for debugging and analyzing the system's behavior.
By completing this stage, you'll have a robust proof of concept that demonstrates the potential of your LLM application in a real-world business context, setting the stage for potential wider adoption or further development.
Stage 5 : Production
Objective
Learn about
How to design scalability system
SLA and norm
Deployment
This stage is crucial if you aim to deploy your LLM application with scalability in mind. After successfully completing your Proof of Concept, you'll need to address several additional tasks to prepare for production.
RAG system optimization, improve the speed of your RAG system to achieve response times of less than 5 seconds per question (request).
Conduct thorough UAT to ensure your system meets all requirements and user expectations. Once approved, deploy your RAG system to the production environment.
Post-Production Tasks:
Implement robust monitoring systems, establish appropriate guardrails, and ensure your application complies with relevant regulations and industry standards.
Learn about scaling your system to handle increased loads. Understand the cost implications of scaling and explore strategies to reduce operational costs.
Apply your knowledge to scale your RAG system effectively.
Develop and implement strategies to monetize your LLM application. This may include subscription models, pay-per-use systems, or other revenue-generating approaches.
Congratulations !
You've now completed your journey to creating a potential LLM application. Throughout this project, you've undoubtedly gained valuable knowledge and experience. However, if you have any further questions or topics you'd like to discuss, we're here to help. Please don't hesitate to contact us (discord) anytime - we'll be glad to assist you and will respond as quickly as possible!
This journey is just the beginning, and we look forward to hearing about your continued success and innovations in the world of LLM applications.
Last updated
